Modeling of Responses and Response Times with the Package<b>cirt</b>

Jean‐Paul Fox, Rinke Klein Entink, Wim van der Linden

Journal of Statistical Software · 2007 · 128 citations · 17 references

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Concepts

TL;DR

In computerized testing, both responses and response times are recorded, and their relationship is complex, varying from a speed–accuracy tradeoff at the individual level to a positive correlation across populations. The study proposes a conjoint model to explore and test hypotheses about the relationship between response times and accuracies. Item responses are modeled with a two‑parameter normal‑ogive IRT model, response times with a lognormal model, and the two are combined in a hierarchical framework nested within individuals, with estimation via MCMC implemented in an R package. All parameters of the conjoint model can be estimated simultaneously using an MCMC approach.

Abstract

In computerized testing, the test takers' responses as well as their response times on the items are recorded. The relationship between response times and response accuracies is complex and varies over levels of observation. For example, it takes the form of a tradeoff between speed and accuracy at the level of a fixed person but may become a positive correlation for a population of test takers. In order to explore such relationships and test hypotheses about them, a conjoint model is proposed. Item responses are modeled by a two-parameter normal-ogive IRT model and response times by a lognormal model. The two models are combined using a hierarchical framework based on the fact that response times and responses are nested within individuals. All parameters can be estimated simultaneously using an MCMC estimation approach. A R-package for the MCMC algorithm is presented and explained.

References

17